Faculty Publications

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    Reliability Analysis of Exponential Models Based on Skewness and Kurtosis
    (Springer India, 2015) Roopashri Tantri, B.; Murulidhar, N.N.
    Every field in modern era is computerized. As the requirements of software increase, competitions among the manufacturers of software also increase. Thus, there is a need for reliable software. Software reliability is defined as the probability of failure-free operation of software in a specified environment for a specific period of time. Thus, if T denotes the failure time of software, then, its reliability, denoted by R(t), is given by R(t) = P(T *gt; t). Various models of software reliability have been developed. One such model is the exponential class model. For such a model, the reliability function is given by R(t) = фe-фt, where ф is the failure rate. Various estimates of reliability have been obtained for this class of models. The most commonly used method is the method of Maximum Likelihood Estimation (MLE). But it is not as efficient as the Minimum Variance Unbiased Estimation (MVUE). In our previous work, we obtained this minimum variance unbiased estimator for the reliability function R(t) and proved its efficiency by comparing it with the Maximum likelihood estimator. We used variance as a measure of comparison. But variance is only a second order measure. In this paper, we are trying to enhance our work further by comparing higher order measures. We are also trying to analyze the same using skewness and kurtosis. © Springer India 2015.
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    Software reliability estimation of gamma failure time models
    (Institute of Electrical and Electronics Engineers Inc., 2017) Tantri, B.R.; Murulidhar, N.N.
    With the increasing role of software in every field, concern has grown over the quality of software products. One such measure of software quality is the reliability, which is the probability of failure-free operation of a computer program in a specified environment for a specified time. Prior to the release of software, failure data are obtained during testing, using which, future reliability of software can be assessed. Reliability assessment can be done using various measures like Mean Time To Failure, failure intensity function, mean value function, etc. To assess the reliability, one should have a mathematical model that describes the behavior of failure with time. Such models are called software reliability models. Several classes of software reliability models have been defined based on the failure time distribution. One such class of models is the gamma failure time models, where failure times are assumed to follow gamma distribution. In this paper, software reliability estimates of gamma failure time models have been obtained using the method of Maximum Likelihood Estimation and method of Minimum Variance Unbiased Estimation. Using these methods, reliability of the software at a future time point can be estimated. Case studies have been considered to compare the two estimates. © 2016 IEEE.
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    Software Reliability Estimation of Schick-Wolverton Rayleigh Failure Time Model
    (International Society of Science and Applied Technologies, 2021) Tantri, R.; Murulidhar, N.
    It is well known that the users and the developers of the software often encounter the problem of its quality and durability. Software reliability is a measure of the quality of the software. By estimating the reliability of the software, the developers can ensure that the reliability objectives as specified by the user are met. On the other hand, the reliability estimate also enables the users to decide whether or not to accept the software. Thus, reliability estimates are the key factors in decision making problems for both the developers and the users of the software. Software reliability models with specified failure time distributions are considered and failure data obtained during testing are used to estimate their reliability. Herein, Rayleigh failure time distribution of Schick-Wolverton is considered and reliability estimates have been obtained by the methods of Minimum Variance Unbiased Estimation (MVUE) and Maximum Likelihood Estimation (MLE). The two estimates have been compared. Case studies have also been considered and the above two estimates of reliability have been compared. It is observed that the estimator obtained using the method of minimum variance unbiased estimation provides a better estimator than that obtained using the method of maximum likelihood estimation. © 2021 Proceedings - 26th ISSAT International Conference on Reliability and Quality in Design, RQD 2021. All rights reserved.
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    Novel Software Reliability Estimate for Exponential Class Models
    (International Society of Science and Applied Technologies, 2022) Murulidhar, N.N.; Tantri, B.R.
    Increasing usage of software in every domain has raised concern over its quality and durability. Many indicators for measuring the quality and durability of the software exist. One such indicator is the software reliability, which is a measure of the life time of the software. Estimation of software reliability enables the users of the software to decide whether or not to accept the software. Knowing the probability distribution of the failure times of the software, the reliability of the software can be estimated. Herein, software reliability models having exponential failure times have been considered. The reliability has been estimated by considering the methods of Maximum Likelihood Estimation (MLE) and Minimum Variance Unbiased Estimation (MVUE). The two estimators are combined to obtain the Improved Estimator (IM). Few data sets have been considered and the estimates have been obtained using the said three methods. The three estimators are then compared using the coefficient of variation. It is observed that the Improved Estimator possesses the least value of coefficient of variation, thus indicating that the Improved Estimator is better as compared to the other two estimators and hence provides more accurate estimate of reliability. © 2022 International Society of Science and Applied Technologies
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    Life data analysis of server virtualized system
    (GEOMATE International Society geomate@gi-j.com, 2017) Mohan, B.R.; Guddeti, G.
    The use of reliability metrics and life data analysis has received considerable attention recently in the software engineering literature. Life data analysis under the actual operational profile can, however, be expensive, time consuming or even infeasible. In this paper, a systematic approach has been adopted in order to reduce the experimentation time for estimating time to failure of a server virtualized system. The study of time to failure (TTF) is very essential in server virtualized system, because it is the crux of the cloud computing infrastructure. In order to meet service-level agreements (SLAs) like availability, reliability and response time, prediction of reliability metrics like mean time to failure (MTTF), life distribution etc are indispensable. The most important contributions of this paper are the reduction of experimental time, and the life data analysis of the server virtualized systems which were not addressed so far. Experimental results demonstrate that there is only four percentage deviation from the observed results from the Normalized Root Mean Square Error and resulting in 96% accuracy of predicting MTTF. © Int. J. of GEOMATE.
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    Application ANN Tool for Validation of LHD Machine Performance Characteristics
    (Springer, 2020) Balaraju, B.; Raj, G.R.; Murthy, C.S.
    Survival of industries has become more critical in the present global competitive business environment unless they produce their projected production levels. The accomplishment of this can be possible only by maintaining the men and machinery in an efficient and effective manner. Hence, it is more essential to estimate the performance of utilized equipment for reaching/achieving future goals. The present study focuses on the estimation of underground mining machinery such as the load–haul–dump machine performance characteristics using ‘Isograph Reliability Workbench 13.0’ software. The allocation of best-fit/goodness-of-fit distribution was made by utilizing the Kolmogorov–Smirnov test (K–S) test. The parameters were recorded based on the best-fitted results using the maximum likelihood estimate test. Further, a feed-forward-back-propagation artificial neural network (ANN) tool has been used to develop the models of reliability, availability and preventive maintenance time intervals. The number of neurons was selected with the Levenberg–Marquardt learning algorithm in the hidden layer as the optimal value. The output responses were predicted corresponding to the optimal values. Further, an attempt has been made to validate the computed results with ANN predicted responses. The recommendations are suggested to the industry based on the results for the improvement of system performance. © 2020, The Institution of Engineers (India).
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    Energy- and Reliability-Aware Provisioning of Parallelized Service Function Chains With Delay Guarantees
    (Institute of Electrical and Electronics Engineers Inc., 2024) Chintapalli, V.R.; Killi, B.R.; Partani, R.; Tamma, B.R.; Siva Ram Murthy, C.
    Network Functions Virtualization (NFV) leverages virtualization and cloud computing technologies to make networks more flexible, manageable, and scalable. Instead of using traditional hardware middleboxes, NFV uses more flexible Virtual Network Functions (VNFs) running on commodity servers. One of the key challenges in NFV is to ensure strict reliability and low latency while also improving energy efficiency. Any software or hardware failures in an NFV environment can disrupt the service provided by a chain of VNFs, known as a Service Function Chain (SFC), resulting in significant data loss, delays, and wasted resources. Due to the sequential nature of SFC, latency increases linearly with the number of VNFs. To address this issue, researchers have proposed parallelized SFC or VNF parallelization, which allows multiple independent VNFs in an SFC to run in parallel. In this work, we propose a method to solve the parallelized SFC deployment problem as an Integer Linear Program (ILP) that minimizes energy consumption while ensuring reliability and delay constraints. Since the problem is NP-hard, we also propose a heuristic scheme named ERASE that determines the placement of VNFs and routes traffic through them in a way that minimizes energy consumption while meeting capacity, reliability, and delay requirements. The effectiveness of ERASE is evaluated through extensive simulations and it is shown to perform better than benchmark schemes in terms of total energy consumption and reliability achieved. © 2017 IEEE.